{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "bf5fc6d9",
   "metadata": {},
   "source": [
    "# NBA球员数据分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5b8727ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9cb4346e",
   "metadata": {},
   "source": [
    "## 1 获取数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "bacd8d10",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Rk</th>\n",
       "      <th>PLAYER</th>\n",
       "      <th>POSITION</th>\n",
       "      <th>AGE</th>\n",
       "      <th>MP</th>\n",
       "      <th>FG</th>\n",
       "      <th>FGA</th>\n",
       "      <th>FG%</th>\n",
       "      <th>3P</th>\n",
       "      <th>3PA</th>\n",
       "      <th>...</th>\n",
       "      <th>GP</th>\n",
       "      <th>MPG</th>\n",
       "      <th>ORPM</th>\n",
       "      <th>DRPM</th>\n",
       "      <th>RPM</th>\n",
       "      <th>WINS_RPM</th>\n",
       "      <th>PIE</th>\n",
       "      <th>PACE</th>\n",
       "      <th>W</th>\n",
       "      <th>SALARY_MILLIONS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Russell Westbrook</td>\n",
       "      <td>PG</td>\n",
       "      <td>28</td>\n",
       "      <td>34.6</td>\n",
       "      <td>10.2</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.425</td>\n",
       "      <td>2.5</td>\n",
       "      <td>7.2</td>\n",
       "      <td>...</td>\n",
       "      <td>81</td>\n",
       "      <td>34.6</td>\n",
       "      <td>6.74</td>\n",
       "      <td>-0.47</td>\n",
       "      <td>6.27</td>\n",
       "      <td>17.34</td>\n",
       "      <td>23.0</td>\n",
       "      <td>102.31</td>\n",
       "      <td>46</td>\n",
       "      <td>26.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>James Harden</td>\n",
       "      <td>PG</td>\n",
       "      <td>27</td>\n",
       "      <td>36.4</td>\n",
       "      <td>8.3</td>\n",
       "      <td>18.9</td>\n",
       "      <td>0.440</td>\n",
       "      <td>3.2</td>\n",
       "      <td>9.3</td>\n",
       "      <td>...</td>\n",
       "      <td>81</td>\n",
       "      <td>36.4</td>\n",
       "      <td>6.38</td>\n",
       "      <td>-1.57</td>\n",
       "      <td>4.81</td>\n",
       "      <td>15.54</td>\n",
       "      <td>19.0</td>\n",
       "      <td>102.98</td>\n",
       "      <td>54</td>\n",
       "      <td>26.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Isaiah Thomas</td>\n",
       "      <td>PG</td>\n",
       "      <td>27</td>\n",
       "      <td>33.8</td>\n",
       "      <td>9.0</td>\n",
       "      <td>19.4</td>\n",
       "      <td>0.463</td>\n",
       "      <td>3.2</td>\n",
       "      <td>8.5</td>\n",
       "      <td>...</td>\n",
       "      <td>76</td>\n",
       "      <td>33.8</td>\n",
       "      <td>5.72</td>\n",
       "      <td>-3.89</td>\n",
       "      <td>1.83</td>\n",
       "      <td>8.19</td>\n",
       "      <td>16.1</td>\n",
       "      <td>99.84</td>\n",
       "      <td>51</td>\n",
       "      <td>6.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Anthony Davis</td>\n",
       "      <td>C</td>\n",
       "      <td>23</td>\n",
       "      <td>36.1</td>\n",
       "      <td>10.3</td>\n",
       "      <td>20.3</td>\n",
       "      <td>0.505</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.8</td>\n",
       "      <td>...</td>\n",
       "      <td>75</td>\n",
       "      <td>36.1</td>\n",
       "      <td>0.45</td>\n",
       "      <td>3.90</td>\n",
       "      <td>4.35</td>\n",
       "      <td>12.81</td>\n",
       "      <td>19.2</td>\n",
       "      <td>100.19</td>\n",
       "      <td>31</td>\n",
       "      <td>22.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>DeMarcus Cousins</td>\n",
       "      <td>C</td>\n",
       "      <td>26</td>\n",
       "      <td>34.2</td>\n",
       "      <td>9.0</td>\n",
       "      <td>19.9</td>\n",
       "      <td>0.452</td>\n",
       "      <td>1.8</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>72</td>\n",
       "      <td>34.2</td>\n",
       "      <td>3.56</td>\n",
       "      <td>0.64</td>\n",
       "      <td>4.20</td>\n",
       "      <td>11.26</td>\n",
       "      <td>17.8</td>\n",
       "      <td>97.11</td>\n",
       "      <td>30</td>\n",
       "      <td>16.96</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Rk             PLAYER POSITION  AGE    MP    FG   FGA    FG%   3P  3PA  \\\n",
       "0   1  Russell Westbrook       PG   28  34.6  10.2  24.0  0.425  2.5  7.2   \n",
       "1   2       James Harden       PG   27  36.4   8.3  18.9  0.440  3.2  9.3   \n",
       "2   3      Isaiah Thomas       PG   27  33.8   9.0  19.4  0.463  3.2  8.5   \n",
       "3   4      Anthony Davis        C   23  36.1  10.3  20.3  0.505  0.5  1.8   \n",
       "4   6   DeMarcus Cousins        C   26  34.2   9.0  19.9  0.452  1.8  5.0   \n",
       "\n",
       "   ...  GP   MPG  ORPM  DRPM   RPM  WINS_RPM   PIE    PACE   W  \\\n",
       "0  ...  81  34.6  6.74 -0.47  6.27     17.34  23.0  102.31  46   \n",
       "1  ...  81  36.4  6.38 -1.57  4.81     15.54  19.0  102.98  54   \n",
       "2  ...  76  33.8  5.72 -3.89  1.83      8.19  16.1   99.84  51   \n",
       "3  ...  75  36.1  0.45  3.90  4.35     12.81  19.2  100.19  31   \n",
       "4  ...  72  34.2  3.56  0.64  4.20     11.26  17.8   97.11  30   \n",
       "\n",
       "   SALARY_MILLIONS  \n",
       "0            26.50  \n",
       "1            26.50  \n",
       "2             6.59  \n",
       "3            22.12  \n",
       "4            16.96  \n",
       "\n",
       "[5 rows x 38 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\".\\\\Data\\\\nba_2017_nba_players_with_salary.csv\")\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "30a4103c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(342, 38)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "35ab0f65",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Rk</th>\n",
       "      <th>AGE</th>\n",
       "      <th>MP</th>\n",
       "      <th>FG</th>\n",
       "      <th>FGA</th>\n",
       "      <th>FG%</th>\n",
       "      <th>3P</th>\n",
       "      <th>3PA</th>\n",
       "      <th>3P%</th>\n",
       "      <th>2P</th>\n",
       "      <th>...</th>\n",
       "      <th>GP</th>\n",
       "      <th>MPG</th>\n",
       "      <th>ORPM</th>\n",
       "      <th>DRPM</th>\n",
       "      <th>RPM</th>\n",
       "      <th>WINS_RPM</th>\n",
       "      <th>PIE</th>\n",
       "      <th>PACE</th>\n",
       "      <th>W</th>\n",
       "      <th>SALARY_MILLIONS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>342.000000</td>\n",
       "      <td>342.000000</td>\n",
       "      <td>342.000000</td>\n",
       "      <td>342.000000</td>\n",
       "      <td>342.000000</td>\n",
       "      <td>342.000000</td>\n",
       "      <td>342.000000</td>\n",
       "      <td>342.000000</td>\n",
       "      <td>320.000000</td>\n",
       "      <td>342.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>342.000000</td>\n",
       "      <td>342.000000</td>\n",
       "      <td>342.000000</td>\n",
       "      <td>342.000000</td>\n",
       "      <td>342.000000</td>\n",
       "      <td>342.000000</td>\n",
       "      <td>342.000000</td>\n",
       "      <td>342.000000</td>\n",
       "      <td>342.000000</td>\n",
       "      <td>342.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>217.269006</td>\n",
       "      <td>26.444444</td>\n",
       "      <td>21.572515</td>\n",
       "      <td>3.483626</td>\n",
       "      <td>7.725439</td>\n",
       "      <td>0.446096</td>\n",
       "      <td>0.865789</td>\n",
       "      <td>2.440058</td>\n",
       "      <td>0.307016</td>\n",
       "      <td>2.620175</td>\n",
       "      <td>...</td>\n",
       "      <td>58.198830</td>\n",
       "      <td>21.572807</td>\n",
       "      <td>-0.676023</td>\n",
       "      <td>-0.005789</td>\n",
       "      <td>-0.681813</td>\n",
       "      <td>2.861725</td>\n",
       "      <td>9.186842</td>\n",
       "      <td>98.341053</td>\n",
       "      <td>28.950292</td>\n",
       "      <td>7.294006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>136.403138</td>\n",
       "      <td>4.295686</td>\n",
       "      <td>8.804018</td>\n",
       "      <td>2.200872</td>\n",
       "      <td>4.646933</td>\n",
       "      <td>0.078992</td>\n",
       "      <td>0.780010</td>\n",
       "      <td>2.021716</td>\n",
       "      <td>0.134691</td>\n",
       "      <td>1.828714</td>\n",
       "      <td>...</td>\n",
       "      <td>22.282015</td>\n",
       "      <td>8.804121</td>\n",
       "      <td>2.063237</td>\n",
       "      <td>1.614293</td>\n",
       "      <td>2.522014</td>\n",
       "      <td>3.880914</td>\n",
       "      <td>3.585475</td>\n",
       "      <td>2.870091</td>\n",
       "      <td>14.603876</td>\n",
       "      <td>6.516326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>2.200000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.200000</td>\n",
       "      <td>-4.430000</td>\n",
       "      <td>-3.920000</td>\n",
       "      <td>-6.600000</td>\n",
       "      <td>-2.320000</td>\n",
       "      <td>-1.600000</td>\n",
       "      <td>87.460000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.030000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>100.250000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>15.025000</td>\n",
       "      <td>1.800000</td>\n",
       "      <td>4.225000</td>\n",
       "      <td>0.402250</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.280250</td>\n",
       "      <td>1.200000</td>\n",
       "      <td>...</td>\n",
       "      <td>43.500000</td>\n",
       "      <td>15.025000</td>\n",
       "      <td>-2.147500</td>\n",
       "      <td>-1.222500</td>\n",
       "      <td>-2.422500</td>\n",
       "      <td>0.102500</td>\n",
       "      <td>7.100000</td>\n",
       "      <td>96.850000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>2.185000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>205.500000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>21.650000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>6.700000</td>\n",
       "      <td>0.442000</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>2.200000</td>\n",
       "      <td>0.340500</td>\n",
       "      <td>2.200000</td>\n",
       "      <td>...</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>21.650000</td>\n",
       "      <td>-0.990000</td>\n",
       "      <td>-0.130000</td>\n",
       "      <td>-1.170000</td>\n",
       "      <td>1.410000</td>\n",
       "      <td>8.700000</td>\n",
       "      <td>98.205000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>4.920000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>327.750000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>29.075000</td>\n",
       "      <td>4.700000</td>\n",
       "      <td>10.400000</td>\n",
       "      <td>0.481000</td>\n",
       "      <td>1.400000</td>\n",
       "      <td>3.600000</td>\n",
       "      <td>0.373500</td>\n",
       "      <td>3.700000</td>\n",
       "      <td>...</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>29.075000</td>\n",
       "      <td>0.257500</td>\n",
       "      <td>1.067500</td>\n",
       "      <td>0.865000</td>\n",
       "      <td>4.487500</td>\n",
       "      <td>10.900000</td>\n",
       "      <td>100.060000</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>11.110000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>482.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>37.800000</td>\n",
       "      <td>10.300000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>4.100000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>9.700000</td>\n",
       "      <td>...</td>\n",
       "      <td>82.000000</td>\n",
       "      <td>37.800000</td>\n",
       "      <td>7.270000</td>\n",
       "      <td>6.020000</td>\n",
       "      <td>8.420000</td>\n",
       "      <td>20.430000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>109.870000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>30.960000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               Rk         AGE          MP          FG         FGA         FG%  \\\n",
       "count  342.000000  342.000000  342.000000  342.000000  342.000000  342.000000   \n",
       "mean   217.269006   26.444444   21.572515    3.483626    7.725439    0.446096   \n",
       "std    136.403138    4.295686    8.804018    2.200872    4.646933    0.078992   \n",
       "min      1.000000   19.000000    2.200000    0.000000    0.800000    0.000000   \n",
       "25%    100.250000   23.000000   15.025000    1.800000    4.225000    0.402250   \n",
       "50%    205.500000   26.000000   21.650000    3.000000    6.700000    0.442000   \n",
       "75%    327.750000   29.000000   29.075000    4.700000   10.400000    0.481000   \n",
       "max    482.000000   40.000000   37.800000   10.300000   24.000000    0.750000   \n",
       "\n",
       "               3P         3PA         3P%          2P  ...          GP  \\\n",
       "count  342.000000  342.000000  320.000000  342.000000  ...  342.000000   \n",
       "mean     0.865789    2.440058    0.307016    2.620175  ...   58.198830   \n",
       "std      0.780010    2.021716    0.134691    1.828714  ...   22.282015   \n",
       "min      0.000000    0.000000    0.000000    0.000000  ...    2.000000   \n",
       "25%      0.200000    0.800000    0.280250    1.200000  ...   43.500000   \n",
       "50%      0.700000    2.200000    0.340500    2.200000  ...   66.000000   \n",
       "75%      1.400000    3.600000    0.373500    3.700000  ...   76.000000   \n",
       "max      4.100000   10.000000    1.000000    9.700000  ...   82.000000   \n",
       "\n",
       "              MPG        ORPM        DRPM         RPM    WINS_RPM         PIE  \\\n",
       "count  342.000000  342.000000  342.000000  342.000000  342.000000  342.000000   \n",
       "mean    21.572807   -0.676023   -0.005789   -0.681813    2.861725    9.186842   \n",
       "std      8.804121    2.063237    1.614293    2.522014    3.880914    3.585475   \n",
       "min      2.200000   -4.430000   -3.920000   -6.600000   -2.320000   -1.600000   \n",
       "25%     15.025000   -2.147500   -1.222500   -2.422500    0.102500    7.100000   \n",
       "50%     21.650000   -0.990000   -0.130000   -1.170000    1.410000    8.700000   \n",
       "75%     29.075000    0.257500    1.067500    0.865000    4.487500   10.900000   \n",
       "max     37.800000    7.270000    6.020000    8.420000   20.430000   23.000000   \n",
       "\n",
       "             PACE           W  SALARY_MILLIONS  \n",
       "count  342.000000  342.000000       342.000000  \n",
       "mean    98.341053   28.950292         7.294006  \n",
       "std      2.870091   14.603876         6.516326  \n",
       "min     87.460000    0.000000         0.030000  \n",
       "25%     96.850000   19.000000         2.185000  \n",
       "50%     98.205000   29.000000         4.920000  \n",
       "75%    100.060000   39.000000        11.110000  \n",
       "max    109.870000   66.000000        30.960000  \n",
       "\n",
       "[8 rows x 35 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "091ba54c",
   "metadata": {},
   "source": [
    "## 2 数据分析\n",
    "### 2.1 数据相关性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "36e71ffa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RPM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>SALARY_MILLIONS</th>\n",
       "      <th>ORB</th>\n",
       "      <th>DRB</th>\n",
       "      <th>TRB</th>\n",
       "      <th>AST</th>\n",
       "      <th>STL</th>\n",
       "      <th>BLK</th>\n",
       "      <th>TOV</th>\n",
       "      <th>PF</th>\n",
       "      <th>POINTS</th>\n",
       "      <th>GP</th>\n",
       "      <th>MPG</th>\n",
       "      <th>ORPM</th>\n",
       "      <th>DRPM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6.27</td>\n",
       "      <td>28</td>\n",
       "      <td>26.50</td>\n",
       "      <td>1.7</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10.7</td>\n",
       "      <td>10.4</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>31.6</td>\n",
       "      <td>81</td>\n",
       "      <td>34.6</td>\n",
       "      <td>6.74</td>\n",
       "      <td>-0.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.81</td>\n",
       "      <td>27</td>\n",
       "      <td>26.50</td>\n",
       "      <td>1.2</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.1</td>\n",
       "      <td>11.2</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.7</td>\n",
       "      <td>29.1</td>\n",
       "      <td>81</td>\n",
       "      <td>36.4</td>\n",
       "      <td>6.38</td>\n",
       "      <td>-1.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.83</td>\n",
       "      <td>27</td>\n",
       "      <td>6.59</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2.1</td>\n",
       "      <td>2.7</td>\n",
       "      <td>5.9</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.2</td>\n",
       "      <td>2.8</td>\n",
       "      <td>2.2</td>\n",
       "      <td>28.9</td>\n",
       "      <td>76</td>\n",
       "      <td>33.8</td>\n",
       "      <td>5.72</td>\n",
       "      <td>-3.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.35</td>\n",
       "      <td>23</td>\n",
       "      <td>22.12</td>\n",
       "      <td>2.3</td>\n",
       "      <td>9.5</td>\n",
       "      <td>11.8</td>\n",
       "      <td>2.1</td>\n",
       "      <td>1.3</td>\n",
       "      <td>2.2</td>\n",
       "      <td>2.4</td>\n",
       "      <td>2.2</td>\n",
       "      <td>28.0</td>\n",
       "      <td>75</td>\n",
       "      <td>36.1</td>\n",
       "      <td>0.45</td>\n",
       "      <td>3.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.20</td>\n",
       "      <td>26</td>\n",
       "      <td>16.96</td>\n",
       "      <td>2.1</td>\n",
       "      <td>8.9</td>\n",
       "      <td>11.0</td>\n",
       "      <td>4.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1.3</td>\n",
       "      <td>3.7</td>\n",
       "      <td>3.9</td>\n",
       "      <td>27.0</td>\n",
       "      <td>72</td>\n",
       "      <td>34.2</td>\n",
       "      <td>3.56</td>\n",
       "      <td>0.64</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    RPM  AGE  SALARY_MILLIONS  ORB  DRB   TRB   AST  STL  BLK  TOV   PF  \\\n",
       "0  6.27   28            26.50  1.7  9.0  10.7  10.4  1.6  0.4  5.4  2.3   \n",
       "1  4.81   27            26.50  1.2  7.0   8.1  11.2  1.5  0.5  5.7  2.7   \n",
       "2  1.83   27             6.59  0.6  2.1   2.7   5.9  0.9  0.2  2.8  2.2   \n",
       "3  4.35   23            22.12  2.3  9.5  11.8   2.1  1.3  2.2  2.4  2.2   \n",
       "4  4.20   26            16.96  2.1  8.9  11.0   4.6  1.4  1.3  3.7  3.9   \n",
       "\n",
       "   POINTS  GP   MPG  ORPM  DRPM  \n",
       "0    31.6  81  34.6  6.74 -0.47  \n",
       "1    29.1  81  36.4  6.38 -1.57  \n",
       "2    28.9  76  33.8  5.72 -3.89  \n",
       "3    28.0  75  36.1  0.45  3.90  \n",
       "4    27.0  72  34.2  3.56  0.64  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_cor = data.loc[:, ['RPM', 'AGE', 'SALARY_MILLIONS', 'ORB', 'DRB', 'TRB',\n",
    "                       'AST', 'STL', 'BLK', 'TOV', 'PF', 'POINTS', 'GP', 'MPG', 'ORPM', 'DRPM']]\n",
    "data_cor.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "898c0692",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>RPM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>SALARY_MILLIONS</th>\n",
       "      <th>ORB</th>\n",
       "      <th>DRB</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>RPM</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.175820</td>\n",
       "      <td>0.477542</td>\n",
       "      <td>0.388764</td>\n",
       "      <td>0.623515</td>\n",
       "      <td>0.587853</td>\n",
       "      <td>0.481971</td>\n",
       "      <td>0.599008</td>\n",
       "      <td>0.463097</td>\n",
       "      <td>0.492014</td>\n",
       "      <td>0.434226</td>\n",
       "      <td>0.604432</td>\n",
       "      <td>0.340810</td>\n",
       "      <td>0.549449</td>\n",
       "      <td>0.769822</td>\n",
       "      <td>0.578388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AGE</th>\n",
       "      <td>0.175820</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.353312</td>\n",
       "      <td>-0.015752</td>\n",
       "      <td>0.088859</td>\n",
       "      <td>0.062064</td>\n",
       "      <td>0.114908</td>\n",
       "      <td>0.069892</td>\n",
       "      <td>-0.062917</td>\n",
       "      <td>0.030673</td>\n",
       "      <td>0.005512</td>\n",
       "      <td>0.031422</td>\n",
       "      <td>0.051863</td>\n",
       "      <td>0.099657</td>\n",
       "      <td>0.136177</td>\n",
       "      <td>0.100636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SALARY_MILLIONS</th>\n",
       "      <td>0.477542</td>\n",
       "      <td>0.353312</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.264954</td>\n",
       "      <td>0.531569</td>\n",
       "      <td>0.482088</td>\n",
       "      <td>0.486159</td>\n",
       "      <td>0.446763</td>\n",
       "      <td>0.260288</td>\n",
       "      <td>0.536993</td>\n",
       "      <td>0.341512</td>\n",
       "      <td>0.635425</td>\n",
       "      <td>0.348093</td>\n",
       "      <td>0.594162</td>\n",
       "      <td>0.503682</td>\n",
       "      <td>0.102307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ORB</th>\n",
       "      <td>0.388764</td>\n",
       "      <td>-0.015752</td>\n",
       "      <td>0.264954</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.731345</td>\n",
       "      <td>0.861103</td>\n",
       "      <td>-0.011632</td>\n",
       "      <td>0.169075</td>\n",
       "      <td>0.654265</td>\n",
       "      <td>0.274670</td>\n",
       "      <td>0.557957</td>\n",
       "      <td>0.284908</td>\n",
       "      <td>0.296975</td>\n",
       "      <td>0.342140</td>\n",
       "      <td>0.102113</td>\n",
       "      <td>0.476857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DRB</th>\n",
       "      <td>0.623515</td>\n",
       "      <td>0.088859</td>\n",
       "      <td>0.531569</td>\n",
       "      <td>0.731345</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.976244</td>\n",
       "      <td>0.350786</td>\n",
       "      <td>0.485726</td>\n",
       "      <td>0.660733</td>\n",
       "      <td>0.598043</td>\n",
       "      <td>0.670708</td>\n",
       "      <td>0.648267</td>\n",
       "      <td>0.473376</td>\n",
       "      <td>0.684662</td>\n",
       "      <td>0.428433</td>\n",
       "      <td>0.426536</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      RPM       AGE  SALARY_MILLIONS       ORB       DRB  \\\n",
       "RPM              1.000000  0.175820         0.477542  0.388764  0.623515   \n",
       "AGE              0.175820  1.000000         0.353312 -0.015752  0.088859   \n",
       "SALARY_MILLIONS  0.477542  0.353312         1.000000  0.264954  0.531569   \n",
       "ORB              0.388764 -0.015752         0.264954  1.000000  0.731345   \n",
       "DRB              0.623515  0.088859         0.531569  0.731345  1.000000   \n",
       "\n",
       "                      TRB       AST       STL       BLK       TOV        PF  \\\n",
       "RPM              0.587853  0.481971  0.599008  0.463097  0.492014  0.434226   \n",
       "AGE              0.062064  0.114908  0.069892 -0.062917  0.030673  0.005512   \n",
       "SALARY_MILLIONS  0.482088  0.486159  0.446763  0.260288  0.536993  0.341512   \n",
       "ORB              0.861103 -0.011632  0.169075  0.654265  0.274670  0.557957   \n",
       "DRB              0.976244  0.350786  0.485726  0.660733  0.598043  0.670708   \n",
       "\n",
       "                   POINTS        GP       MPG      ORPM      DRPM  \n",
       "RPM              0.604432  0.340810  0.549449  0.769822  0.578388  \n",
       "AGE              0.031422  0.051863  0.099657  0.136177  0.100636  \n",
       "SALARY_MILLIONS  0.635425  0.348093  0.594162  0.503682  0.102307  \n",
       "ORB              0.284908  0.296975  0.342140  0.102113  0.476857  \n",
       "DRB              0.648267  0.473376  0.684662  0.428433  0.426536  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 相关性\n",
    "corr = data_cor.corr()\n",
    "# 获取两列数据的相关性\n",
    "corr.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "dfc136d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制热力图\n",
    "sns.heatmap(corr, square= True, linewidths= 0.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3efb01d9",
   "metadata": {},
   "source": [
    "## 2.2基本数据排名分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "64faadc6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PLAYER</th>\n",
       "      <th>RPM</th>\n",
       "      <th>AGE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>LeBron James</td>\n",
       "      <td>8.42</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Chris Paul</td>\n",
       "      <td>7.92</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Stephen Curry</td>\n",
       "      <td>7.41</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>Draymond Green</td>\n",
       "      <td>7.14</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Kawhi Leonard</td>\n",
       "      <td>7.08</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             PLAYER   RPM  AGE\n",
       "6      LeBron James  8.42   32\n",
       "37       Chris Paul  7.92   31\n",
       "8     Stephen Curry  7.41   28\n",
       "120  Draymond Green  7.14   26\n",
       "7     Kawhi Leonard  7.08   25"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 按照效率排名\n",
    "data.loc[:,[\"PLAYER\",\"RPM\",\"AGE\"]].sort_values(by=\"RPM\",ascending=False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "bef5b238",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PLAYER</th>\n",
       "      <th>RPM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>SALARY_MILLIONS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>LeBron James</td>\n",
       "      <td>8.42</td>\n",
       "      <td>32</td>\n",
       "      <td>30.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Mike Conley</td>\n",
       "      <td>4.47</td>\n",
       "      <td>29</td>\n",
       "      <td>26.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>Al Horford</td>\n",
       "      <td>1.82</td>\n",
       "      <td>30</td>\n",
       "      <td>26.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Russell Westbrook</td>\n",
       "      <td>6.27</td>\n",
       "      <td>28</td>\n",
       "      <td>26.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Kevin Durant</td>\n",
       "      <td>5.74</td>\n",
       "      <td>28</td>\n",
       "      <td>26.50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               PLAYER   RPM  AGE  SALARY_MILLIONS\n",
       "6        LeBron James  8.42   32            30.96\n",
       "25        Mike Conley  4.47   29            26.54\n",
       "67         Al Horford  1.82   30            26.54\n",
       "0   Russell Westbrook  6.27   28            26.50\n",
       "10       Kevin Durant  5.74   28            26.50"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 按照球员薪资排名\n",
    "data.loc[:,[\"PLAYER\",\"RPM\",\"AGE\",\"SALARY_MILLIONS\"]].sort_values(by=\"SALARY_MILLIONS\",ascending=False).head()"
   ]
  }
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